Following previous works [1]}, [2]}, [3]}, [4]}, [5]}, we also choose the AWGN as our research object due to its extensiveness and practicality.
During training, we use DIV2K [6]} as our training dataset since it is a high-quality image restoration dataset.
As for the test datasets, we use Set12 [7]}, BSD68 [8]}, and Urban100 [9]} for grayscale image denoising and choose Kodak24 [10]}, CBSD68 [8]}, and Urban100 [9]} for the color image denoising.
Meanwhile, in order to further verify the effectiveness and robustness of MDRN, we utilize RN6 [13]} and
RN15 [13]} to test the ability of MDRN for real noise removing.
